How to Use a Chi Square Test in Likert Scales

In survey research, a Likert scale is an approach to response categories that measures the extent of a person’s satisfaction or agreement with a set of statements or questions. This type of response category makes it easy to quantify survey responses, simplifying data analysis. A variety of options for analysing Likert scale data exists, including the chi square statistic, which compares respondents’ actual responses with expected answers. Chi square assesses the statistical significance of a given hypothesis. The greater the level of deviation between actual and expected responses, the higher the chi square statistic and, thus, the less well the results fit the hypothesis.

Combine the response categories in your Likert scale. For example, if your Likert scale uses the response categories of strongly agree, agree, disagree, strongly disagree, neither agree nor disagree, combine the agree and strongly agree responses into one category and the disagree and strongly disagree into another. This gives you three categories of responses: agree, disagree and neither.

Run the chi square statistical test, using your spreadsheet program or statistical software. To find the test in Excel, for example, click the “Formulas” tab at the top of your spreadsheet, then choose “More Functions” and select “Statistical,” which displays the variety of available procedures. “Chitest” is the chi square procedure. Clicking on a cell and dragging the mouse over the range of data you want analysed tells Excel the data on which to conduct the chi square test.

Examine the results of your chi square test generated by your spreadsheet or statistical program. When reviewing results, pay close attention to the size of the chi square statistic and the level of statistical significance. A higher chi square statistic indicates greater variation between observed and expected responses. Most spreadsheet and statistical programs use a significance level of .05, meaning that there is only a 5 per cent chance that the statistical significance, if any, resulted from random chance.

Interpret the results of your analysis. Remember that chi square indicates whether a statistically significant relationship exists but does not reveal information about the strength of that relationship.